{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:51:19Z","timestamp":1769881879918,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T00:00:00Z","timestamp":1559174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of National Key Research and Development Program of China","award":["2016YFC0500902"],"award-info":[{"award-number":["2016YFC0500902"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201704910491"],"award-info":[{"award-number":["201704910491"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) &lt; 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R2 = 0.947; ground validation, R2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems.<\/jats:p>","DOI":"10.3390\/rs11111286","type":"journal-article","created":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T11:07:44Z","timestamp":1559214464000},"page":"1286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China"],"prefix":"10.3390","volume":"11","author":[{"given":"Xiang","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Arid Land Research Center, Tottori University, Tottori 680-0001, Japan"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shulin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-4898","authenticated-orcid":false,"given":"Fei","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"International Platform for Dryland Research and Education, Tottori University, Tottori 680-0001, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7690-0633","authenticated-orcid":false,"given":"Atsushi","family":"Tsunekawa","sequence":"additional","affiliation":[{"name":"Arid Land Research Center, Tottori University, Tottori 680-0001, Japan"}]},{"given":"Wenping","family":"Kang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zichen","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Kun","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Belnap, J., and Lange, O.L. 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